Adaptive Decision Feedback Equalizer (DFE) algorithms play an important role in compensating for distortion and noise in Single Input Single Output (SISO) communication systems. In this comparative analysis, we reviewed some of the most popular adaptive DFE algorithms, including the Least Mean Square (LMS), Recursive Least Squares (RLS), Sign-Sign LMS (SSLMS), and Normalized Least Mean Square (NLMS) algorithms. Each algorithm has its own advantages and disadvantages, and the choice of algorithm depends on the specific requirements of the communication system. The LMS algorithm is a good choice for low-complexity systems, while the RLS algorithm is suitable for larger systems with more computational resources. The SSLMS algorithm is a good compromise between the LMS and NLMS algorithms, while the NLMS algorithm is suitable for systems that require faster convergence and improved stability.In this paper, a comparative analysis is contemplated for different Adaptive Decision Feedback Equalizer (ADFE) algorithms for the architecture design of Soft-Input Soft-Output (SISO) equalizer. Various adaptive algorithms execute on Equalizer. An adaptive algorithm employs to update all filter coefficients in the design which significantly reduces bit error. According to the requirement of data rate, the selection of a suitable adaptive equalizer algorithm is devised. The selection of accessible step size enables representation in the frequency domain so that the proposed adaptive algorithm can converge in mean square perception. Simulation results present that the proposed realization exhibits better bit error rate (BER) performance compared to other realizations. It also reduces computational intricacy with a better convergence rate.

Soft-Input Soft-Output (SISO) equalizer; Fast Block LMS algorithm; Normalized Sign Least Mean Square algorithm; Bit Error Rate